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Hiroyuki Nakahara1, Sivaramakrishnan Kaveri

  • 1Laboratory for Integrated Theoretical Neuroscience, RIKEN Brain Science Institute,Wako, Saitama, 351-0198 Japan. hiro@brain.riken.jp

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This study introduces internal-time temporal difference (TD) learning, separating neural valuation time from experimental time. This novel framework better explains time representation in decision-making processes and neural activity.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Temporal difference (TD) learning models value-based decision making and neural activity.
  • The representation of time in neural processes within TD frameworks is not well understood.

Purpose of the Study:

  • To propose and characterize a new TD formulation, internal-time TD, that distinguishes between internal (neural valuation) and conventional (experimental) time.
  • To explore the implications of this internal-time TD model for neural decision-making processes.

Main Methods:

  • Developed a theoretical TD formulation based on internal time.
  • Analyzed the mathematical properties and computational characteristics of internal-time TD.
  • Examined the model's predictions for intertemporal choice tasks and the effects of internal time noise.

Main Results:

  • Internal-time TD computations, like TD error, vary based on time frame and unit due to the separation of operator and observer times.
  • The internal-time TD value function demonstrates both exponential and hyperbolic discounting in intertemporal choice tasks.
  • Internal time noise and its dynamic construction influence TD error and can be modulated by serotonin function.

Conclusions:

  • The internal-time TD model offers a more nuanced understanding of time representation in neural decision-making.
  • This framework provides a basis for further research into interval timing, subjective time, and the role of neuromodulators like serotonin.